Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Todd C. Pataky
Title: rft1d: Smooth One-Dimensional Random Field Upcrossing Probabilities in Python
Abstract: Through topological expectations regarding smooth, thresholded n-dimensional Gaussian continua, random field theory (RFT) describes probabilities associated with both the field-wide maximum and threshold-surviving upcrossing geometry. A key application of RFT is a correction for multiple comparisons which affords field-level hypothesis testing for both univariate and multivariate fields. For unbroken isotropic fields just one parameter in addition to the mean and variance is required: the ratio of a field's size to its smoothness. Ironically the simplest manifestation of RFT (1D unbroken fields) has rarely surfaced in the literature, even during its foundational development in the late 1970s. This Python package implements 1D RFT primarily for exploring and validating RFT expectations, but also describes how it can be applied to yield statistical inferences regarding sets of experimental 1D fields.

Page views:: 2010. Submitted: 2014-08-25. Published: 2016-07-26.
Paper: rft1d: Smooth One-Dimensional Random Field Upcrossing Probabilities in Python     Download PDF (Downloads: 1468)
Supplements: Source code Download (Downloads: 89; 163KB) Python replication code Download (Downloads: 169; 39KB)

DOI: 10.18637/jss.v071.i07

This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.